Cargando…
Machine learning in nephrology: scratching the surface
Machine learning shows enormous potential in facilitating decision-making regarding kidney diseases. With the development of data preservation and processing, as well as the advancement of machine learning algorithms, machine learning is expected to make remarkable breakthroughs in nephrology. Machi...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wolters Kluwer Health
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7190222/ https://www.ncbi.nlm.nih.gov/pubmed/32049747 http://dx.doi.org/10.1097/CM9.0000000000000694 |
_version_ | 1783527646379900928 |
---|---|
author | Li, Qi Fan, Qiu-Ling Han, Qiu-Xia Geng, Wen-Jia Zhao, Huan-Huan Ding, Xiao-Nan Yan, Jing-Yao Zhu, Han-Yu |
author_facet | Li, Qi Fan, Qiu-Ling Han, Qiu-Xia Geng, Wen-Jia Zhao, Huan-Huan Ding, Xiao-Nan Yan, Jing-Yao Zhu, Han-Yu |
author_sort | Li, Qi |
collection | PubMed |
description | Machine learning shows enormous potential in facilitating decision-making regarding kidney diseases. With the development of data preservation and processing, as well as the advancement of machine learning algorithms, machine learning is expected to make remarkable breakthroughs in nephrology. Machine learning models have yielded many preliminaries to moderate and several excellent achievements in the fields, including analysis of renal pathological images, diagnosis and prognosis of chronic kidney diseases and acute kidney injury, as well as management of dialysis treatments. However, it is just scratching the surface of the field; at the same time, machine learning and its applications in renal diseases are facing a number of challenges. In this review, we discuss the application status, challenges and future prospects of machine learning in nephrology to help people further understand and improve the capacity for prediction, detection, and care quality in kidney diseases. |
format | Online Article Text |
id | pubmed-7190222 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Wolters Kluwer Health |
record_format | MEDLINE/PubMed |
spelling | pubmed-71902222020-08-05 Machine learning in nephrology: scratching the surface Li, Qi Fan, Qiu-Ling Han, Qiu-Xia Geng, Wen-Jia Zhao, Huan-Huan Ding, Xiao-Nan Yan, Jing-Yao Zhu, Han-Yu Chin Med J (Engl) Review Articles Machine learning shows enormous potential in facilitating decision-making regarding kidney diseases. With the development of data preservation and processing, as well as the advancement of machine learning algorithms, machine learning is expected to make remarkable breakthroughs in nephrology. Machine learning models have yielded many preliminaries to moderate and several excellent achievements in the fields, including analysis of renal pathological images, diagnosis and prognosis of chronic kidney diseases and acute kidney injury, as well as management of dialysis treatments. However, it is just scratching the surface of the field; at the same time, machine learning and its applications in renal diseases are facing a number of challenges. In this review, we discuss the application status, challenges and future prospects of machine learning in nephrology to help people further understand and improve the capacity for prediction, detection, and care quality in kidney diseases. Wolters Kluwer Health 2020-03-20 2020-03-20 /pmc/articles/PMC7190222/ /pubmed/32049747 http://dx.doi.org/10.1097/CM9.0000000000000694 Text en Copyright © 2020 The Chinese Medical Association, produced by Wolters Kluwer, Inc. under the CC-BY-NC-ND license. http://creativecommons.org/licenses/by-nc-nd/4.0 This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0 |
spellingShingle | Review Articles Li, Qi Fan, Qiu-Ling Han, Qiu-Xia Geng, Wen-Jia Zhao, Huan-Huan Ding, Xiao-Nan Yan, Jing-Yao Zhu, Han-Yu Machine learning in nephrology: scratching the surface |
title | Machine learning in nephrology: scratching the surface |
title_full | Machine learning in nephrology: scratching the surface |
title_fullStr | Machine learning in nephrology: scratching the surface |
title_full_unstemmed | Machine learning in nephrology: scratching the surface |
title_short | Machine learning in nephrology: scratching the surface |
title_sort | machine learning in nephrology: scratching the surface |
topic | Review Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7190222/ https://www.ncbi.nlm.nih.gov/pubmed/32049747 http://dx.doi.org/10.1097/CM9.0000000000000694 |
work_keys_str_mv | AT liqi machinelearninginnephrologyscratchingthesurface AT fanqiuling machinelearninginnephrologyscratchingthesurface AT hanqiuxia machinelearninginnephrologyscratchingthesurface AT gengwenjia machinelearninginnephrologyscratchingthesurface AT zhaohuanhuan machinelearninginnephrologyscratchingthesurface AT dingxiaonan machinelearninginnephrologyscratchingthesurface AT yanjingyao machinelearninginnephrologyscratchingthesurface AT zhuhanyu machinelearninginnephrologyscratchingthesurface |